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INDONESIA
Media Statistika
Published by Universitas Diponegoro
ISSN : -     EISSN : 24770647     DOI : -
Core Subject : Science,
Decision Sciences, Operations Research & Management
Arjuna Subject : -
Articles 11 Documents
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Search results for , issue "Vol 17, No 2 (2024): Media Statistika" : 11 Documents clear
A COMPARISON OF MULTIPLICATIVE AND ADDITIVE HAZARD MODELS USING THE HAZARD AND SURVIVAL RATIO Danardono, Danardono; Gunardi, Gunardi
MEDIA STATISTIKA Vol 17, No 2 (2024): Media Statistika
Publisher : Department of Statistics, Faculty of Science and Mathematics, Universitas Diponegoro

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.14710/medstat.17.2.140-149

Abstract

The Cox multiplicative hazards regression and Aalen additive hazards regression models are widely used for survival data analysis. While the Cox model emphasizes hazard ratios or relative risks, the Aalen model focuses on relative survival or excess risks. This study compares the performance of these models through simulations of biomedical survival data. Results reveal no clear dominance of one model over the other, suggesting that both models should be employed to have a more thorough survival analysis.
EXPLORE THE DETERMINANTS OF CUSTOMERS TIME TO PAY HOUSE OWNERSHIP LOAN ON DATA WITH HIGH MULTICOLLINEARITY WITH PCA-COX REGRESSION Ramadhan, Rangga; Fimba, Adfi Bio; Fernandes, Adji Achmad Rinaldo; Solimun, Solimun; Junianto, Fachira Haneinanda; Amanda, Devi Veda; Sumara, Rauzan
MEDIA STATISTIKA Vol 17, No 2 (2024): Media Statistika
Publisher : Department of Statistics, Faculty of Science and Mathematics, Universitas Diponegoro

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.14710/medstat.17.2.117-127

Abstract

One of the models in survival analysis is the Cox proportional hazards model. This method ignores assumptions regarding the distribution of survival times studied. If there are indications of multicollinearity in data handling, one way that can be done is to use PCA (Principal Component Analysis). PCA-Cox regression is a combination of survival analysis and PCA which can be an alternative in analyzing multicollinearity survival data. The large number of cases of bad credit means that customers must be careful in providing credit to prospective customers. Character, capacity, capital and collateral variables are thought to influence the length of time customers pay house ownership loans at the bank. The data used is secondary data (n=100) regarding the assessment of character variables, capacity, capital and collateral, credit collectibility, and time to pay customer house ownership loans at the bank. The results of the analysis using PCA-Cox regression show that the variables character, capacity, capital and collateral have a significant effect on the length of house ownership loan payment time for Bank X customers. The originality of this research is the use of the PCA-Cox regression integration model in bank credit risk analysis.
APPLICATION OF THE DYNAMIC FACTOR MODEL ON NOWCASTING SECTORAL ECONOMIC GROWTH WITH HIGH-FREQUENCY DATA Supriyatna, Putu Krishnanda; Prastyo, Dedy Dwi; Akbar, Muhammad Sjahid
MEDIA STATISTIKA Vol 17, No 2 (2024): Media Statistika
Publisher : Department of Statistics, Faculty of Science and Mathematics, Universitas Diponegoro

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.14710/medstat.17.2.128-139

Abstract

Economic growth is crucial for planning, yet delayed data releases challenge timely decision-making. Nowcasting offers near-real-time insights using high-frequency indicators (released monthly, weekly, or even daily) to predict low-frequency variables (quarterly or yearly). This study uses high-frequency indicators (monthly), such as stock price changes, air quality, transportation data, financial conditions, and Google Trends, to nowcast quarterly GDP through the Dynamic Factor Model (DFM). The data used span from January 2010 until March 2023, which is split into two: January 2010 until March 2022 for training data and the rest as testing data. Compared to the benchmark Autoregressive Moving Average with Exogenous Variables (ARMAX) model, DFM demonstrates superior accuracy with lower symmetric Mean Absolute Percentage Error (sMAPE). In addition, to evaluate the model performance in nowcasting the GDP across the sector using DFM, the additional metrics, i.e., Root Mean Square Error (RMSE), Mean Absolute Deviation (MAD), and Adjusted R-squared, concluded that in the industrial and transportation sectors results in sufficient nowcasting of GDP, Meanwhile, In the financial sector, the results of the nowcasting GDP give poor estimation results that need improvement.
PRICING RESIDENTIAL EARTHQUAKE INSURANCE IN INDONESIA Anwar, Alief Glenfico; Qoyyimi, Danang Teguh; Putra, Hengki Eko
MEDIA STATISTIKA Vol 17, No 2 (2024): Media Statistika
Publisher : Department of Statistics, Faculty of Science and Mathematics, Universitas Diponegoro

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.14710/medstat.17.2.150-161

Abstract

Adaptive Social Protection (ASP) is a framework that integrates social protection, disaster risk reduction, and climate change adaptation to enhance resilience against shocks and hazards. As a country vulnerable to earthquakes, Indonesia faces threats of losses due to seismic disasters. The national budget available to cover these losses can only address 13.6% of the total disaster-related losses. This study proposes an earthquake insurance scheme to protect all residences in Indonesia as part of the ASP framework, followed by the calculation of premium rates for this insurance scheme. This study utilizes the built-in OpenQuake calculator known as the probabilistic event-based risk calculator to simulate annual earthquake losses over a period of 10,000 years. The negative binomial distribution and the Pareto IV distribution are assessed as the most optimal models in modeling frequency and severity through distribution fitting. The application of collective risk models and the principle of pure premium results in a pure premium rate of 0.3994073 ‰. This pure premium rate can serve as a starting point in the establishment of comprehensive residential earthquake insurance in Indonesia.
COMPARISON OF SARIMA AND HIGH-ORDER FUZZY TIME SERIES CHEN TO PREDICT KALLA KARS MOTORBIKE SALES Syam, Ummul Auliyah; Irdayanti, Irdayanti; Notodiputro, Khairil Anwar; Angraini, Yenni; Mualifah, Laily Nissa Atul
MEDIA STATISTIKA Vol 17, No 2 (2024): Media Statistika
Publisher : Department of Statistics, Faculty of Science and Mathematics, Universitas Diponegoro

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.14710/medstat.17.2.197-208

Abstract

Forecasting sales time series data is essential for companies to support effective planning and decision-making processes. This study evaluates the strengths of the Seasonal Autoregressive Integrated Moving Average (SARIMA) and High-Order Fuzzy Time Series Chen (FTS Chen) models in predicting motorbike sales at Kalla Kars Company, a prominent automotive dealer in Sulawesi, Indonesia. SARIMA is renowned for accurately capturing seasonal patterns, while the FTS Chen model excels in handling data uncertainties and incorporating complex relationships through high-order fuzzy logic. Weekly sales data from January 2020 to February 2024 were analyzed, with 205 weeks used for training and 13 weeks for testing. The results indicate that the third-order FTS Chen model outperforms SARIMA, achieving a Root Mean Square Error (RMSE) of 1.88 and a Mean Absolute Percentage Error (MAPE) of 4.64%. Forecasts for the next eight weeks using the third-order FTS Chen model suggest a decline in sales, contrasting with the SARIMA model, which predicts a slight increase. These results show that Chen's FTS model is more accurate and reliable, making it an effective choice for forecasting Kalla Kars motorbike sales.
ANALYZING SOCIO-ECONOMIC RECOVERY ON SUMATRA ISLAND POST-COVID-19: A SPATIAL DURBIN MODEL APPROACH Lubis, Ibrah Hasanah; Mahdi, Saiful; Munawar, Munawar
MEDIA STATISTIKA Vol 17, No 2 (2024): Media Statistika
Publisher : Department of Statistics, Faculty of Science and Mathematics, Universitas Diponegoro

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.14710/medstat.17.2.209-220

Abstract

The COVID-19 outbreak was designated as a public health emergency that disturbed the world from January 2020 to May 2023 by the World Health Organization. This outbreak has drastically changed the order of socio-economic life. According to data Gross Domestic Product, it was recorded to grow by 5.03% in 2023 according to data from the Central Statistics Agency, which is still slightly below the pre-pandemic level of 5.17% in 2018. At the regional level, only 6 provinces experienced a higher Gross Regional Domestic Product growth rate in 2023 compared to 2018. These figures highlight the need for recovery efforts to be made to restore the condition of the community and the environment so that the socio-economic activities of the community can run well again. This study uses Google mobility report data and panel data spatial regression analysis to determine the factors that influence socio-economic recovery on the island of Sumatra and how the influence between regions in the recovery effort. The data used is panel data for 273 observation days in eight provinces.  By integrating spatial panel data methods with mobility-based proxies, this approach offers a new framework that is rarely applied in studies of post-COVID-19 socio-economic recovery in Sumatra.
THE ANALYSIS OF SOCIO-ECONOMIC EFFECT ON CRIMINALITY IN INDONESIA USING FUZZY CLUSTERWISE REGRESSION MODEL Azzarah, Dian Fatimah; Mukid, Moch. Abdul; I Maruddani, Di Asih; Rochayani, Masithoh Yessi
MEDIA STATISTIKA Vol 17, No 2 (2024): Media Statistika
Publisher : Department of Statistics, Faculty of Science and Mathematics, Universitas Diponegoro

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.14710/medstat.17.2.221-232

Abstract

Crime in Indonesia has shown a fluctuating trend and has increased significantly in recent years, with striking variations in crime rates between provinces. This phenomenon raises questions about the role of socio-economic factors such as education, poverty, and unemployment in influencing crime rates. Although there have been many studies examining the relationship between these variables and crime, the approaches used often assume that the relationship between variables is homogeneous across regions. In fact, heterogeneity in characteristics between provinces can cause different relationships. Therefore, an analysis approach is needed that can accommodate this diversity. This study proposes the Fuzzy Clusterwise Regression method which not only improves model accuracy compared to classical linear regression (with an increase in the coefficient of determination from 65.72% to more than 90%), but is also able to identify different patterns of relationships between regional groups (clusters). The results from FCR showed that the effect of socio-economic factors on crime varies between clusters and the optimum number of clusters is 4. In cluster 1, cluster 2, and cluster 3 all the variables had a significant influence on the amount of crime. Meanwhile, in cluster 4, the population poverty variable has no significant effect on the crime rate.
UNCERTAINTY ANALYSIS OF VOLTAGE MEASUREMENT USING ATMEGA328P MICROCONTROLLER: AN ANOVA TEST APPROACH Julian, James; Fauzi, Ade Fikri; Dewantara, Annastya Bagas; Ulhaq, Faiz Daffa; Wahyuni, Fitri
MEDIA STATISTIKA Vol 17, No 2 (2024): Media Statistika
Publisher : Department of Statistics, Faculty of Science and Mathematics, Universitas Diponegoro

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.14710/medstat.17.2.173-184

Abstract

The voltage sensors are widely used in various applications. In certain applications, such as medical devices, autonomous vehicles, or the military, the sensor's accuracy and level of precision play an important role, making it necessary to evaluate the sensor's performance. In this research, testing of direct current (DC) voltage sensors was carried out using analysis of variance (ANOVA) and Tukey honestly significant difference (HSD) to test sensor performance in various voltage ranges. This research used an experimental-based quantitative approach, using the ATmega328P. Data collection begins by calibrating the analog-to-digital converter (ADC) readings against voltage values with linear regression, the Chauvenet criterion to eliminate outlier data caused by noise from the environment, One-way ANOVA is used to determine differences in variations between voltage distances, and a Q-Q plot is used to determine the normality of the sensor error distribution. This research obtained from Tukey-HSD that 9 comparisons accepting the null hypothesis (H0). And 27 pairs accepting the alternate hypothesis (H1). The data was found to be normally distributed through the calculation of residual ANOVA, and visualization of data with the Q-Q plot, and the use of the sensor was effective in the range of 3V to 24.5V.
PARAMETER INDEPENDENT FUZZY WEIGHTED k-NEAREST NEIGHBOR Mayawi, Mayawi; Subanar, Subanar
MEDIA STATISTIKA Vol 17, No 2 (2024): Media Statistika
Publisher : Department of Statistics, Faculty of Science and Mathematics, Universitas Diponegoro

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.14710/medstat.17.2.162-172

Abstract

Parameter Independent Fuzzy Weighted k-Nearest Neighbor (PIFWkNN) as a classification technique developed by combining Success History based Parameter Adaptive Differential Evolution (SHADE) with Fuzzy k-Nearest Neighbor (FkNN), where this PIFWkNN does not state the optimization of weights and k values as two separate problems, but they’re combined into one and solved simultaneously by the SHADE algorithm. The steps for implementing the PIFWkNN method are explained, followed by its application to 10 different datasets, and then the accuracy is calculated. To see the consistency of the goodness of the classification of this method, the accuracy results are compared with the accuracy of the k-Nearest Neighbor (kNN), FkNN, and Weighted k-Nearest Neighbor (WkNN). The results show that the average accuracy of PIFWkNN, kNN, FkNN, and WkNN is 75.76%, 68.52%, 71.40% and 66.22% so PIFWkNN is higher than the three methods. Using the Wilcoxon Sign Rank (WSR) test also concluded that with a 95% confidence shows that every hypothesis had significant differences. Furthermore, it descriptively shows that the average rank of PIFWkNN is higher than the other. Thus, the PIFWkNN has higher accuracy than the kNN, FkNN, and WkNN.
IS THE BOX-COX TRANSFORMATION NEEDED IN MODELING TELKOM’S STOCK PRICE USING NNAR AND DESH METHODS? Noven, Michela Sheryl; Respatiwulan, Respatiwulan; Sulandari, Winita
MEDIA STATISTIKA Vol 17, No 2 (2024): Media Statistika
Publisher : Department of Statistics, Faculty of Science and Mathematics, Universitas Diponegoro

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.14710/medstat.17.2.185-196

Abstract

Accurate stock price forecasting requires appropriate preprocessing, particularly for time series data with high variability and nonlinear patterns. This study investigates whether applying the Box-Cox Transformation (BCT) improves forecasting performance when modeling Telkom Indonesia's stock price using Neural Network Autoregressive (NNAR) and Double Exponential Smoothing Holt (DESH) methods. The NNAR model architecture is selected based on nonlinearity testing of lag variables, while DESH parameters are optimized by minimizing mean square error. Forecasting accuracy is evaluated using Mean Absolute Percentage Error (MAPE), root Mean Square Error (RMSE), and Mean Percentage Error (MPE), comparing models built with and without BCT. Results show that BCT does not enhance forecasting accuracy for either NNAR or DESH. Moreover, the NNAR model without BCT outperforms DESH, producing approximately 50% lower MAPE, RMSE, and MPE values on the testing dataset. These findings suggest that BCT may not be necessary for time series modeling in this case, and NNAR without transformation is recommended for forecasting Telkom's stock price.

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